
\subsection{Case study}

Here, we present the GAMAVI model, which aims at understanding and evaluating the impact of the environment on the dynamic of avian influenza among poultry flocks in North Vietnam at the village scale. The simulation has been implemented on the GAMA platform \cite{GAMA:2010}. This model provides an apparatus that allows epidemiologists to
build fully controlled experiments in order to test and explore hypotheses that could neither be conducted on the field nor in laboratory. Practically, experiments focus on the influence of the poultry production, the environment (topography and dynamics) and their interactions \cite{Amouroux:2010}.

In this participatory version of GAMAVI, we have defined three roles with specific visualization and access rights:
\begin{itemize}
    \item \emph{Local\_Authority}: accesses only to parameters and outputs related to natural and social characteristics of their village;
    \item \emph{Epidemiologist}: accesses only to parameters and outputs related to the epidemiology;
    \item \emph{Farmer}: accesses only to parameters and outputs on the movement of poultry and on the production dynamics (\emph{i.e.} periodicity of renewal).
\end{itemize}	
Practically, people representing local authorities provide the statistical data about natural and social characteristics of the village while epidemiologists have data and hypotheses on the epidemics dynamics. Natural data include topography of the environment, temperature and seasonality. Social characteristics of a village consist mainly of the number and types of farms and the organization and the dynamics of the production. Epidemics are mostly described using direct infectiousness, persistence in the environment and disease consequences on the individual (morbidity, mortality, duration of infection and so on). In addition, epidemiologists have the theoretical background required to understand the dynamics of the disease and to evaluate which parameters have to be changed in order to mitigate the virus diffusion and which mitigation measures are possible to do the same. Finally, farmers are experts of poultry production dynamics (farm management) and daily behaviors of the poultries (pasture, rest, movement, etc.).
%
The collaboration between these three kinds of thematicians is important and actually necessary because of their complementary skills and knowledge.

Figures \ref{fig:Fig5_localAuthority}, \ref{fig:Fig5_epydemio} and \ref{fig:Fig5_farmer} present the interface managing parameter changes such that respectively the local authorities, the epydemiologists and the farmers can see it. Figures highlight the fact that each participant, due to the role affected by the modeler, can only modify their affected parameters and only observe displays that are detailed in the role file for this model. In addition, monitors and graphs of each role have the same customization for displays.


\begin{figure}[!t]
\centering
\includegraphics[width=\linewidth]{imgs/Fig5-interface-localAutority}
\caption{Local authority interface}
\label{fig:Fig5_localAuthority}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=\linewidth]{imgs/Fig5-interface-epydemio}
\caption{Epydemiologist interface}
\label{fig:Fig5_epydemio}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=\linewidth]{imgs/Fig5-interface-Farmer}
\caption{Farmer interface}
\label{fig:Fig5_farmer}
\end{figure}


\subsection{Benefits of our framework}

From the standalone GAMAVI simulator, we successfully create a participative simulator taking into account stackolder competences and goals. A social analysis gives us enough elements to determine a social network and communication way between participants. Given this analysis, a configuration XML file has been defined and uploaded on our groupware.

The originality of our participative framework is to provide a native integration of collaborative tools. It allows to emulate real communication channels (phone, videoconference, ...) used in the real life. Simulators could be customized by collaborative tools according to the aim of the participative game. Indeed, it permits to limit available communication channels and to define how participants collaborate together. It reproduces exchange restriction in the real life.

The simplicity to plug a simulator and to dedicate it for a participative use should be noticed. Our framework provides a web infrastructure and exchange features that permit to transform a standalone simulator into a web compliant simulator offering collaborative tools. In fact, simulators can be deployed on our portal without lot of developments and customized by supported collaborative tools. Developers do not have to redevelop these tools for each simulator and they can thus focus on the simulator design.


%The strategy we provide in this paper permit to limit available communication channel and how stackholders collaborate together.

%A CONTINUEER......

%Multi-agent systems are designed to study phenomena concerning a community of individual by modeling individual behaviors. Guyot \emph{et al.} \cite{GUYOT:2006} merged multi-agent systems and role playing games under the term of participatory multi-agent simulations by modeling collective behaviors. Therefore, an interface aiming at being very useful for participatory simulation must support the collaboration amongst experts to explore the collective behavior and the interaction between an expert and an agent to improve agents behaviors. With the current interface of the PAMS, it is difficult for actors to model accurately the behavior of each agent because the interface is not specialized for each participants' role. We cannot really get the best behavior if the interface does not allow experts to choose the best decisions.

%Moreover, the behaviors in the individual context are different from in the collective context. Indeed, in an individual context, one participant gives his decision only according knowledge in his specialized field of expertise  such as the local authorities, the epidemiologists, and the farmers in model GAMAVI. Thanks to their individual experiences, results of the teamwork are better. In a collective context, the decision of a participant can influence the decisions of the others. For example, the parameter of the water volume (controlled by the local authority) influences to compute the concentration of virus or ponds depletion rate (realized by the epidemiologist). The possibility to observe such a collectively induced behavior is only possible thanks to collaborative tools.

%In addition to the possibility to explore collective behaviors, the multiple interface added in PAMS is also very important duringthe development of the simulator. Indeed, the maturity of the agent behavior directly affects the outcome of the simulation \cite{GUYOT_THESE:2006}. In the model GAMAVI, if only the epidemiologists participate to the simulation development by tackling only the reasons of the propagation of the H5N1 virus, they cannot get reliable results: they cannot understand the behavior and the structures of poultries, they do not participate to pasture these poultries. They only have knowledge on epidemic.

%Moreover a multiple interface allows thematicians in the executive phase to play a particular role to validate and improve the simulation \cite{GUYOT_AAMAS:2006}; this phase is often the place  for negotiations. The above scenario shows that the multi-agent simulations provide a participatory method for exploring and studying collective behavior. The use of negotiation between multidisciplinary participants can help to explicit the individual behaviors in a collaborative context \cite{GUYOT_THESE:2006}. These results are based on discussions after the experiments. If we construct a mechanism to trace and record all interactive changes of motivations of the agent, we can obtain and analyze the results and then extract meaningful consequences. After that we could construct a cognitive agent (with learning machine) and an assistant agent for a more complete participatory simulation. The verbalization phase will use these results to improve the paregoric quality and the decision-making quality. The collective behavior that can be extracted from playing and exchanging the roles formalize better individual strategies such as in \cite{GUYOT_AAMAS:2006}, \cite {Minh:2008} or \cite {Becu:2006}. In the GAMAVI, the negotiation amongst the farmers help to discover a better solution to isolate effectively the flocks of a farmer and the flocks of neighborhoods.

%The different roles used to test and implement the methods can benefit to other disciplines. The research of behavior of agents in mixed communities is very important. It is necessary to resolve rivalries in a collective community. The work presented here constitutes a first step in the design of participatory agent-based simulations between members of the community. Each participant will take his responsibility and become an agent or set of agents. 